Machine Learning Models of Plastic Flow Based on Representation Theory
- Sandia National Lab. (SNL-CA), Livermore, CA (United States). Mechanics of Materials Dept.
- Sandia National Lab. (SNL-CA), Livermore, CA (United States). Thermal/Fluid Science and Engineering Dept.
We use machine learning (ML) to infer stress and plastic flow rules using data from representative polycrystalline simulations. In particular, we use so-called deep (multilayer) neural networks (NN) to represent the two response functions. The ML process does not choose appropriate inputs or outputs, rather it is trained on selected inputs and output. Likewise, its discrimination of features is crucially connected to the chosen input-output map. Furthermore, we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties. With these developments, we enable rapid model building in real-time with experiments, and guide data collection and feature discovery.
- Research Organization:
- Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); SNL Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- NA0003525
- OSTI ID:
- 1502970
- Report Number(s):
- SAND--2019-2905J; 673466
- Journal Information:
- Computer Modeling in Engineering & Sciences, Journal Name: Computer Modeling in Engineering & Sciences Journal Issue: 3 Vol. 117; ISSN 1526-1492
- Publisher:
- Tech Science PressCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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